def test_pp(self):
pp = PhillipsPerron(self.inflation, lags=12)
assert_almost_equal(pp.stat, -7.8076512, DECIMAL_4)
assert pp.test_type == 'tau'
pp.test_type = 'rho'
assert_almost_equal(pp.stat, -108.1552688, DECIMAL_2)
pp.summary()
def test_pp(self):
pp = PhillipsPerron(self.inflation, lags=12)
assert_almost_equal(pp.stat, -7.8076512, DECIMAL_4)
assert pp.test_type == 'tau'
pp.test_type = 'rho'
assert_almost_equal(pp.stat, -108.1552688, DECIMAL_2)
pp.summary()
def unitroot_test(series):
# Basic statistic
plt.figure()
plt.plot(series)
plot_pacf(series)
# ADF test
# AIC & BIC from lags 12 to 1
print('$p$ & AIC & BIC \\\\')
max_lags = 12
for lags in (max_lags - i for i in range(max_lags)):
ar_model = AutoReg(series, lags, 'n')
res = ar_model.fit()
print(f'{lags} & {round(res.aic, 3)} & {round(res.bic, 3)} \\\\')
# Best lags by `ar_select_order`
sel = ar_select_order(series, max_lags, trend='n')
lags = sel.ar_lags[-1]
print(f'Lags selection: {sel.ar_lags}')
# Start ADF test
adf = ADF(series, lags)
print(adf.summary())
# PP test
pp_tau = PhillipsPerron(series, 3, test_type='tau') # q = 3
pp_rho = PhillipsPerron(series, 3, test_type='rho') # q = 3
print(pp_tau.summary())
print(pp_rho.summary())
def test_pp(self):
pp = PhillipsPerron(self.inflation, lags=12)
assert_almost_equal(pp.stat, -7.8076512, DECIMAL_4)
assert pp.test_type == "tau"
with pytest.warns(FutureWarning, match="Mutating unit root"):
pp.test_type = "rho"
assert_almost_equal(pp.stat, -108.1552688, DECIMAL_2)
pp.summary()
def PhilipsPerronTest(data, printResults=True, trend=None, lags=None):
options_Trend = trend if trend != None else {'nc','c','ct'}
options_Lags = lags if lags != None else {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}
results = dict()
for column in data.columns:
print("Philips Perron test for column: " + column)
results_Trend = dict()
for option_Trend in options_Trend:
results_Lag = dict()
for option_Lag in options_Lags:
result = PhillipsPerron(data[column].dropna(), trend=option_Trend, lags=option_Lag)
if printResults:
result.summary()
results_Lag[option_Lag] = result
results_Trend[option_Trend] = results_Lag
results[column] = results_Trend
return results
print('Lean Hogs Future kurtosis is {}'.format(lh.kurtosis(axis=0)[0]))
sns.distplot(lh['Close'], color='blue') #density plot
plt.title('1986–2018 Lean Hogs Future return frequency')
plt.xlabel('Possible range of data values')
# Pull up summary statistics
print(lh.describe())
adf = ADF(lh['Close'])
print(adf.summary().as_text())
kpss = KPSS(lh['Close'])
print(kpss.summary().as_text())
dfgls = DFGLS(lh['Close'])
print(dfgls.summary().as_text())
pp = PhillipsPerron(lh['Close'])
print(pp.summary().as_text())
za = ZivotAndrews(lh['Close'])
print(za.summary().as_text())
vr = VarianceRatio(lh['Close'], 12)
print(vr.summary().as_text())
from arch import arch_model
X = 100 * lh
import datetime as dt
am = arch_model(X, p=4, o=0, q=0, vol='Garch', dist='StudentsT')
res = am.fit(last_obs=dt.datetime(2003, 12, 31))
forecasts = res.forecast(horizon=1, start='2004-1-1')
cond_mean = forecasts.mean['2004':]
cond_var = forecasts.variance['2004':] / 31
def get_phillips_perron(timeseries):
from arch.unitroot import PhillipsPerron
pp = PhillipsPerron(timeseries)
print(pp.summary().as_text())
